**Correlation** is a statistical measure that quantifies the degree of association or relationship between two variables. In other words, it helps us understand how two variables tend to move in relation to each other. 

Correlation provides a straightforward way to examine the result. The correlation value falls within the range of [-1; 1]. Refer to the table below:

| **Correlation Value** | **Meaning** |
| --- | --- |
| 1 | Perfect positive correlation: When one value increases, the other also increases, and vice versa. |
| 0 | No correlation: There is no visible relationship between the variables. |
| -1 | Perfect negative correlation: When one value increases, the other decreases, and vice versa. |

**Correlation with Python:**

To calculate correlation, we will use the function `np.corrcoef()` from **NumPy** with two parameters: the data sequences for which we want to find correlation. Take a look at the example:

import pandas as pd
import numpy as np

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/Stores.csv')

# Calculating correlation 
corr = np.corrcoef(df['Store_Area'], df['Items_Available'])[0,1]

print(corr)

Here, we extracted the value at index [0, 1], just like in the case of **covariance**. In the previous chapter, we obtained the value `74955.85`, and interpreting the result of the covariation function can be challenging. However, in this case, we can conclude that the values are strongly related.

This course gives you a basic knowledge of statistics. It is intended for students with basic knowledge of Python syntax as they will be working with NumPy and pandas modules. During the course, you will become familiar with the basic concepts and then gradually move on to more complex concepts and tasks.

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.

Correlation Value	Meaning
1	Perfect positive correlation: When one value increases, the other also increases, and vice versa.
0	No correlation: There is no visible relationship between the variables.
-1	Perfect negative correlation: When one value increases, the other decreases, and vice versa.

Learning Statistics with Python

Correlation